Search Results (27)

Background/Objectives: Infectious diseases caused by Staphylococcus aureus (S. aureus) have become alarming health issues worldwide due to the ever-increasing emergence of multidrug resistance. In silico approaches can accelerate the identification and/or design of versatile antibacterial chemicals with the ability to target multiple S. aureus strains with varying degrees of drug resistance. Here, we develop a perturbation theory machine learning model based on a multilayer perceptron neural network (PTML-MLP) for the prediction and design of versatile virtual inhibitors against S. aureus strains. Methods: To develop the PTML-MLP model, chemical and biological data associated with antibacterial activity against S. aureus strains were retrieved from the ChEMBL database. We applied the Box–Jenkins approach to convert the topological indices into multi-label graph-theoretical indices; the latter were used as inputs for the creation of the PTML-MLP model. Results: The PTML-MLP model exhibited accuracy higher than 80% in both training and test sets. The physicochemical and structural interpretation of the PTML-MLP model was performed through the fragment-based topological design (FBTD) approach. Such interpretations permitted the analysis of different molecular fragments with favorable contributions to the multi-strain antibacterial activity and the design of four new drug-like molecules using different fragments as building blocks. The designed molecules were predicted/confirmed by our PTML model as multi-strain inhibitors of diverse S. aureus strains, thus representing promising chemotypes to be considered for future synthesis and biological testing of versatile anti-S. aureus agents. Conclusions: This work envisages promising applications of PTML modeling for early antibacterial drug discovery and related antimicrobial research areas. Full article

Forecasting using historical time series data has become increasingly important in today’s world. This paper aims to assess the potential for stable positive development within the wholesale and retail trade sector (SK NACE Section G) and the operations of HORTI, Ltd.( Košice, Slovakia), a company within this industry (SK NACE 46.31—wholesale of fruit and vegetables) by predicting three financial indicators: costs, revenues, and earnings before taxes (EBT) (or earnings after taxes (EAT)). We analyze quarterly data from Q1 2009 to Q4 2023 taken from the sector and monthly data from January 2013 to December 2022 for HORTI, Ltd. Through time series analysis, we aim to identify the most suitable model for forecasting the trends in these financial indicators. The study demonstrates that simple legacy forecasting methods, such as exponential smoothing and Box–Jenkins methodology, are sufficient for accurately predicting financial indicators. These models were selected for their simplicity, interpretability, and efficiency in capturing stable trends, and seasonality, especially in sectors with relatively stable financial behavior. The results confirm that traditional Holt–Winters’ and Autoregressive Integrated Moving Average (ARIMA) models can provide reliable forecasts without the need for more complex approaches. While advanced methods, such as GARCH or machine learning, could improve predictions in volatile conditions, the traditional models offer robust, interpretable results that support managerial decision-making. The findings can help managers estimate the financial health of the company and assess risks such as bankruptcy or insolvency, while also acknowledging the limitations of these models in predicting large shifts due to external factors or market disruptions. Full article

This study evaluates the capability of the ARIMA (Auto Regressive Integrated Moving Average) to predict CO₂ emissions in India using data from 1990 to 2023, addressing a critical need for accurate forecasting amid various economic and environmental uncertainties. It is observed that ARIMA yields high accuracy with respect to the prediction, and hence, it is reliable for environmental forecasting. These predictions give policymakers evidence-based information to aid in implementing sustainable climate policies within India. To ensure reliable predictions, the study methodology utilizes the Box–Jenkins approach, which encompasses model identification, estimation, and diagnostic checking. The initial step in the study is the Augmented Dickey–Fuller (ADF) test, which assesses data stationarity as a prerequisite for precise time series forecasting. Model selection is guided by the Akaike Information Criterion (AIC), which balances prediction accuracy with model complexity. The efficiency of the ARIMA model is assessed by comparing the actual observed values to the predicted CO₂ emissions and the results demonstrate ARIMA’s effectiveness in forecasting India’s CO₂ emissions, validated by statistical measures that confirm the model’s robustness. The value of the present study lies in its focused assessment of the relevance of the ARIMA model to the specific environmental and economic context of India, with actionable insight for policymakers. This study enhances prior research by incorporating a focused approach to data-driven policy formulation that increases climate resilience. The establishment of a reliable model for the forecasting of CO₂ will aspire to support informed decision making in environmental policy and help India move forward toward sustainable climate goals. This study only serves to highlight the applicability of ARIMA in terms of environment-based forecasting and permits further emphasis on how much this method can be a useful data-based tool in climate planning. Full article

The current study aims to model the South African crude oil prices using the hybrid of Box-Jenkins autoregressive integrated moving average (ARIMA) and Neural Networks (NNs). This study introduces a hybrid approach to forecasting methods aimed at resolving the issues of lack of precision in forecasting. The proposed methodology includes two models, namely, hybridisation of ARIMA with artificial neural network (ANN)-based Extreme Learning Machine (ELM) and ARIMA with general regression neural network (GRNN) to model both linear and nonlinear simultaneously. The models were compared with the base ARIMA model. The study utilised monthly time series data spanning from January 2021 to March 2023. The formal stationarity test confirmed that the crude oil price series is integrated of order one, I(1). For the linear process, the ARIMA (2,1,2) model was identified as the best fit for the series and successfully passed all diagnostic tests. The ARIMA-ANN-based ELM hybrid model outperformed both the individual ARIMA model and the ARIMA-GRNN hybrid. However, the ARIMA model also showed better performance than the ARIMA-GRNN hybrid, highlighting its strong competitiveness compared to the ARIMA-ANN-based ELM model. The hybrid models are recommended for use by policy makers and practitioners in general. Full article

Time series forecasting plays a critical role in business planning by offering insights for a competitive advantage. This study compared three forecasting methods: the Holt–Winters, Bagging Holt–Winters, and Box–Jenkins methods. Ten datasets exhibiting linear and non-linear trends and clear and ambiguous seasonal patterns were selected for analysis. The Holt–Winters method was tested using seven initial configurations, while the Bagging Holt–Winters and Box–Jenkins methods were also evaluated. The model performance was assessed using the Root-Mean-Square Error (RMSE) to identify the most effective model, with the Mean Absolute Percentage Error (MAPE) used to gauge the accuracy. Findings indicate that the Bagging Holt–Winters method consistently outperformed the other methods across all the datasets. It effectively handles linear and non-linear trends and clear and ambiguous seasonal patterns. Moreover, the seventh initial configurationdelivered the most accurate forecasts for the Holt–Winters method and is recommended as the optimal starting point. Full article

(1) Background: Artificial intelligence (AI) has flourished in recent years. More specifically, generative AI has had broad applications in many disciplines. While mental illness is on the rise, AI has proven valuable in aiding the diagnosis and treatment of mental disorders. However, there is little to no research about precisely how much interest there is in AI technology. (2) Methods: We performed a Google Trends search for “AI and mental health” and compared relative search volume (RSV) indices of “AI”, “AI and Depression”, and “AI and anxiety”. This time series study employed Box–Jenkins time series modeling to forecast long-term interest through the end of 2024. (3) Results: Within the United States, AI interest steadily increased throughout 2023, with some anomalies due to media reporting. Through predictive models, we found that this trend is predicted to increase 114% through the end of the year 2024, with public interest in AI applications being on the rise. (4) Conclusions: According to our study, we found that the awareness of AI has drastically increased throughout 2023, especially in mental health. This demonstrates increasing public awareness of mental health and AI, making advocacy and education about AI technology of paramount importance. Full article

Laboratory incubators are used to maintain and cultivate microbial and cell cultures. In order to ensure suitable growing conditions and to avoid cell injuries and fast rise and settling times, minimum overshoot and undershoot performance indexes should be considered in the controller design for incubators. Therefore, it is important to build proper models to evaluate the performance of the controllers before implementation. In this study, we propose an approach to build a model for a laboratory incubator. In this approach, the incubator is considered a linear time-invariant single-input, single-output system. Four different model structures, namely auto-regressive exogenous, auto-regressive moving average exogenous, output error and Box–Jenkins, are applied for modeling the system. The parameters of the model structures are estimated by using prediction error methods. The performances of the model structures are evaluated in terms of mean squared error, mean absolute error and goodness of fit. Additionally, residue analysis including auto-correlation and cross-correlation plots is provided. Experiments are carried out in two scenarios. In the first scenario, the identification dataset is collected from the unit-step response, while in the second scenario, it is collected from the pseudorandom binary sequence response. The experimental study shows that the Box–Jenkins model achieves an over 90% fit percentage for the first scenario and an over 95% fit percentage for the second scenario. Based on the experimental results, it is concluded that the Box–Jenkins model can be used as a successful model for laboratory incubators. Full article

Thanks to various European directives, individuals are empowered to share and trade electricity within Renewable Energy Communities, enhancing the operational efficiency of local energy systems. The digital transformation of the energy market enables the integration of decentralized energy resources using cloud computing, the Internet of Things, and artificial intelligence. In order to assess the feasibility of new business models based on data-driven solutions, various electricity consumption time series are necessary at this level of aggregation. Since these are currently not yet available in sufficient quality and quantity, and due to data privacy reasons, synthetic time series are essential in the strategic planning of smart grid energy systems. By enabling the simulation of diverse scenarios, they facilitate the integration of new technologies and the development of effective demand response strategies. Moreover, they provide valuable data for assessing novel load forecasting methodologies that are essential to manage energy efficiently and to ensure grid stability. Therefore, this research proposes a methodology to synthesize electricity consumption time series by applying the Box–Jenkins method, an intelligent sampling technique for data augmentation and a probabilistic forecast model. This novel approach emulates the stochastic nature of electricity consumption time series and synthesizes realistic ones of Renewable Energy Communities concerning seasonal as well as short-term variations and stochasticity. Comparing autocorrelations, distributions of values, and principle components of daily sequences between real and synthetic time series, the results exhibit nearly identical characteristics to the original data and, thus, are usable in designing and studying efficient smart grid systems. Full article

Sugar is an important commodity that is used beyond the food industry. It can be produced from sugarcane and sugar beet, depending on the region. Prices worldwide differ due to high volatility, making it difficult to estimate their forecast. Thus, the present work aims to predict the prices of kilograms of sugar from four databases: the European Union, the United States, Brazil, and the world. To achieve this, linear methods from the Box and Jenkins family were employed, together with classic and new approaches of artificial neural networks: the feedforward Multilayer Perceptron and extreme learning machines, and the recurrent proposals Elman Network, Jordan Network, and Echo State Networks considering two reservoir designs. As performance metrics, the MAE and MSE were addressed. The results indicated that the neural models were more accurate than linear ones. In addition, the MLP and the Elman networks stood out as the winners. Full article

Early identification of anomalies (such as leakages or sensor failures) in urban water distribution systems is critical to mitigating water scarcity in cities and is a challenge in water resource management. Several data-driven methods based on machine learning algorithms have been proposed in the literature for leakage detection in urban water distribution systems. Still, most of them are challenging to implement due to their complexity and requirements of vast amounts of reliable data for proper model generation. In addition, the required infrastructure and instrumentation to collect the data needed to train the models could be unaffordable. This paper presents the use and comparison of Autoregressive Integrated Moving Average models and Transfer Function models generated via the Box–Jenkins approach to modeling the water flow in water distribution systems for anomaly detection. The models were fit using water flow data from tanks operating in a branch of the water distribution system of Mexico City. The results showed that both methods helped select the best model type for each variable in the analyzed water branch, with Seasonal ARIMA models achieving a lower mean absolute percentage error than the fitted Transfer Function models. Furthermore, this methodology can be adjusted to different time windows to generate alerts at different rates and does not require a large sample size. The generated anomaly detection models could improve the efficiency of the water distribution system by detecting anomalies such as wrong measurements and water leakages. Full article

Various statistical distributions have played significant roles in financial data analytics in recent decades. Among these, elliptical modeling has gained popularity, while the study and application of skew-elliptical modeling have garnered increased attention in various domains. This paper begins by acknowledging the notable accomplishments and contributions of Professor Chris Heyde in the field of financial data modeling. We provide a comprehensive review of elliptical and skew-elliptical modeling, summarizing the latest advancements. In particular, we focus on the characteristics, estimation methods, and diagnostics of elliptical and skew-elliptical distributions in regression and time series models, as well as copula modeling. Furthermore, we discuss several related applications in regression and time series models, including estimation and diagnostic methods. The main objective of this paper is to address the critical need for accurately identifying the underlying elliptical distribution, whether it is elliptical or skew-elliptical. This identification is essential for conducting local influence diagnostics and employing appropriate regression methods using suitable elliptical modeling techniques. To illustrate this process, we present examples that demonstrate the identification of the elliptical distribution, starting with the Box–Jenkins methodology and progressing to copula modeling. The inclusion of copula modeling is motivated by its effectiveness in conjunction with elliptical and skew-elliptical distributions, as it aids in distinguishing between the two. Ultimately, the findings of this paper offer valuable insights, as correctly determining the elliptical and skew-elliptical distribution enables the application of suitable local influence and regression methods, thereby contributing to financial portfolio management, business analytics, and insurance analytics, ensuring the accurate specification of models. Full article

This paper presents a new approach for modelling nonlinear dynamic processes (NDP). It is based on a nonlinear autoregressive with exogenous (NARX) inputs model structure and a deep convolutional fuzzy system (DCFS). The DCFS is a hierarchical fuzzy structure, which can overcome the deficiency of general fuzzy systems when facing high dimensional data. For relieving the curse of dimensionality, as well as improving approximation performance of fuzzy models, we propose combining the NARX with the DCFS to provide a good approximation of the complex nonlinear dynamic behavior and a fast-training algorithm with ensured convergence. There are three NARX DCFS structures proposed, and the appropriate training algorithm is adapted. Evaluations were performed on a popular benchmark—Box and Jenkin’s gas furnace data set and the four nonlinear dynamic test systems. The experiments show that the proposed NARX DCFS method can be successfully used to identify nonlinear dynamic systems based on external dynamics structures and nonlinear static approximators. Full article

The study investigated the empirical role of past values of Somalia’s GDP growth rates in its future realizations. Using the Box–Jenkins modeling method, the study utilized 250 in-sample quarterly time series data to forecast out-of-the-sample Somali GDP growth rates for fourteen quarters. Balancing between parsimony and fitness criteria of model selection, the study found Autoregressive Integrated Moving Average ARIMA (5,1,2) to be the most appropriate model to estimate and forecast the trajectory of Somali economic growth. The study sourced the GDP growth data from World Bank World Development Indicators (WDI) for the period between 1960 to 2022. The study results predict that Somalia’s GDP will, on average, experience 4 percent quarterly growth rates for the coming three and half years. To solidify the validity of the forecasting results, the study conducted several ARIMA and rolling window diagnostic tests. The model errors proved to be white noise, the moving average (MA) and Autoregressive (AR) components are covariances stationary, and the rolling window test shows model stability within a 95% confidence interval. These optimistic economic growth forecasts represent a policy dividend for the government of Somalia after almost a decade-long stick-and-carrot economic policies between strict IMF fiscal disciplinary measures and World Bank development investments on target projects. The study, however, acknowledges that the developments of current severe droughts, locust infestations, COVID-19 pandemic, internal political, and security stability, and that the active involvement of international development partners will play a crucial role in the realization of these promising growth projections. Full article

Background: Monkeypox virus is gaining attention due to its severity and spread among people. This study sheds light on the modeling and forecasting of new monkeypox cases. Knowledge about the future situation of the virus using a more accurate time series and stochastic models is required for future actions and plans to cope with the challenge. Methods: We conduct a side-by-side comparison of the machine learning approach with the traditional time series model. The multilayer perceptron model (MLP), a machine learning technique, and the Box–Jenkins methodology, also known as the ARIMA model, are used for classical modeling. Both methods are applied to the Monkeypox cumulative data set and compared using different model selection criteria such as root mean square error, mean square error, mean absolute error, and mean absolute percentage error. Results: With a root mean square error of 150.78, the monkeypox series follows the ARIMA (7,1,7) model among the other potential models. Comparatively, we use the multilayer perceptron (MLP) model, which employs the sigmoid activation function and has a different number of hidden neurons in a single hidden layer. The root mean square error of the MLP model, which uses a single input and ten hidden neurons, is 54.40, significantly lower than that of the ARIMA model. The actual confirmed cases versus estimated or fitted plots also demonstrate that the multilayer perceptron model has a better fit for the monkeypox data than the ARIMA model. Conclusions and Recommendation: When it comes to predicting monkeypox, the machine learning method outperforms the traditional time series. A better match can be achieved in future studies by applying the extreme learning machine model (ELM), support vector machine (SVM), and some other methods with various activation functions. It is thus concluded that the selected data provide a real picture of the virus. If the situations remain the same, governments and other stockholders should ensure the follow-up of Standard Operating Procedures (SOPs) among the masses, as the trends will continue rising in the upcoming 10 days. However, governments should take some serious interventions to cope with the virus. Limitation: In the ARIMA models selected for forecasting, we did not incorporate the effect of covariates such as the effect of net migration of monkeypox virus patients, government interventions, etc. Full article

The financial market is a complex system with chaotic behavior that can lead to wild swings within the financial system. This can drive the system into a variety of interesting phenomenon such as phase transitions, bubbles, and crashes, and so on. Of interest in financial modelling is identifying the distribution and the stylized facts of a particular time series, as the distribution and stylized facts can determine if volatility is present, resulting in financial risk and contagion. Regression modelling has been used within this study as a methodology to identify the goodness-of-fit between the original and generated time series model, which serves as a criterion for model selection. Different time series modelling methods that include the common Box–Jenkins ARIMA, ARMA-GARCH type methods, the Geometric Brownian Motion type models and Tsallis entropy based models when data size permits, can use this methodology in model selection. Determining the time series distribution and stylized facts has utility, as the distribution allows for further modelling opportunities such as bivariate regression and copula modelling, apart from the usual forecasting. Determining the distribution and stylized facts also allows for the identification of the parameters that are used within a Geometric Brownian Motion forecasting model. This study has used the Carbon Emissions Futures price between the dates of 1 May 2012 and 1 May 2022, to highlight this application of regression modelling. Full article